"""
# Performance Analysis and Benchmarking Suite

This module provides comprehensive performance analysis tools for PDEJulia.jl
research applications. It enables systematic benchmarking, scaling studies,
and optimization guidance for computational research.

## Key Features

- Comprehensive benchmarking across problem sizes and types
- Scaling analysis (weak and strong scaling)
- Memory profiling and allocation tracking
- Algorithm comparison and selection guidance
- Hardware-specific optimizations
- Performance regression detection
- Automated report generation

## Benchmarking Categories

- Grid Operations: Laplacian, gradient, interpolation
- Time Integration: Various solver methods and step sizes
- Memory Usage: Allocation patterns and optimization
- Parallel Scaling: Multi-core and distributed performance
- Problem-Specific: Pattern formation, phase field, fluid dynamics

## Applications

- Algorithm selection and tuning
- Hardware sizing for research computations
- Performance optimization and bottleneck identification
- Scaling studies for large-scale simulations
- Comparative analysis of numerical methods

## References

- Bailey, D. H. (1991). Twelve ways to fool the masses when giving performance results.
- Dongarra, J. et al. (2003). The LINPACK Benchmark: past, present and future.
- Gustafson, J. L. (1988). Reevaluating Amdahl's law. Communications of the ACM.
"""

using BenchmarkTools, Statistics, Printf
using LinearAlgebra, SparseArrays, FFTW
using Plots, StatsPlots
using JSON, CSV, DataFrames
using Distributed, SharedArrays
using Sys, InteractiveUtils  # For system information

export BenchmarkSuite, PerformanceAnalyzer, ScalingStudy
export run_comprehensive_benchmarks, analyze_performance, generate_performance_report
export compare_algorithms, profile_memory_usage, scaling_analysis

"""
    BenchmarkSuite

Comprehensive benchmarking configuration and results storage.

# Fields
- `name::String`: Benchmark suite identifier
- `categories::Vector{Symbol}`: Categories to benchmark
- `problem_sizes::Vector{Int}`: Grid sizes to test
- `repetitions::Int`: Number of benchmark repetitions
- `warmup_runs::Int`: Warmup iterations for JIT compilation
- `results::Dict`: Collected benchmark results
- `system_info::Dict`: Hardware and software information
"""
mutable struct BenchmarkSuite
    name::String
    categories::Vector{Symbol}
    problem_sizes::Vector{Int}
    repetitions::Int
    warmup_runs::Int
    results::Dict{String, Any}
    system_info::Dict{String, Any}
end

"""
    PerformanceAnalyzer

Analyzes benchmark results and provides optimization recommendations.
"""
struct PerformanceAnalyzer
    suite::BenchmarkSuite
    analysis_methods::Vector{Symbol}
end

"""
    ScalingStudy

Configuration and results for parallel scaling analysis.
"""
struct ScalingStudy
    problem_type::Symbol
    base_size::Int
    scaling_type::Symbol  # :strong or :weak
    core_counts::Vector{Int}
    results::Dict{String, Any}
end

"""
    BenchmarkSuite(name::String; categories=[:all], problem_sizes=[32, 64, 128, 256],
                   repetitions=5, warmup_runs=2)

Create benchmark suite configuration.
"""
function BenchmarkSuite(name::String; 
                       categories=[:all], 
                       problem_sizes=[32, 64, 128, 256],
                       repetitions=5, 
                       warmup_runs=2)
    
    # Expand :all category
    if :all in categories
        categories = [:grid_operations, :time_integration, :memory_usage, 
                     :pattern_formation, :phase_field, :fluid_dynamics]
    end
    
    # Collect system information
    system_info = collect_system_info()
    
    return BenchmarkSuite(name, categories, problem_sizes, repetitions, warmup_runs,
                         Dict{String, Any}(), system_info)
end

"""
    collect_system_info()

Collect comprehensive system information for benchmarking context.
"""
function collect_system_info()
    return Dict(
        "julia_version" => string(VERSION),
        "cpu_info" => Sys.cpu_info()[1],
        "total_memory" => Sys.total_memory(),
        "available_cores" => Sys.CPU_THREADS,
        "os" => string(Sys.KERNEL),
        "architecture" => string(Sys.ARCH),
        "blas_info" => BLAS.get_config(),
        "fftw_version" => FFTW.version,
        "timestamp" => string(now())
    )
end

"""
    run_comprehensive_benchmarks(suite::BenchmarkSuite; verbose=true)

Execute comprehensive benchmark suite across all specified categories.
"""
function run_comprehensive_benchmarks(suite::BenchmarkSuite; verbose=true)
    if verbose
        println("🚀 PDEJulia.jl Performance Benchmarking Suite")
        println("=" * 48)
        println("Suite: $(suite.name)")
        println("Categories: $(suite.categories)")
        println("Problem sizes: $(suite.problem_sizes)")
        println("System: $(suite.system_info["cpu_info"].model)")
        println("Cores: $(suite.system_info["available_cores"])")
        println("Memory: $(round(suite.system_info["total_memory"]/1e9, digits=1)) GB")
        println()
    end
    
    # Initialize results storage
    suite.results["grid_operations"] = Dict()
    suite.results["time_integration"] = Dict()
    suite.results["memory_analysis"] = Dict()
    suite.results["scaling_analysis"] = Dict()
    
    # Run benchmarks by category
    for category in suite.categories
        if verbose
            println("📊 Benchmarking: $category")
            println("-" * 30)
        end
        
        if category == :grid_operations
            benchmark_grid_operations!(suite, verbose)
        elseif category == :time_integration
            benchmark_time_integration!(suite, verbose)
        elseif category == :memory_usage
            benchmark_memory_usage!(suite, verbose)
        elseif category == :pattern_formation
            benchmark_pattern_formation!(suite, verbose)
        elseif category == :phase_field
            benchmark_phase_field!(suite, verbose)
        elseif category == :fluid_dynamics
            benchmark_fluid_dynamics!(suite, verbose)
        end
        
        if verbose
            println()
        end
    end
    
    if verbose
        println("✅ Benchmark suite completed successfully!")
        println()
    end
    
    return suite
end

"""
    benchmark_grid_operations!(suite::BenchmarkSuite, verbose::Bool)

Benchmark fundamental grid operations.
"""
function benchmark_grid_operations!(suite::BenchmarkSuite, verbose::Bool)
    results = Dict{String, Any}()
    
    for n in suite.problem_sizes
        if verbose
            print("  Grid size $n×$n: ")
        end
        
        # Create test data
        u = randn(n, n)
        dx = 1.0 / n
        
        # Laplacian operator benchmarks
        laplacian_fd_time = @belapsed laplacian_finite_difference($u, $dx) samples=suite.repetitions evals=1
        laplacian_fft_time = @belapsed laplacian_fft($u) samples=suite.repetitions evals=1
        
        # Gradient operator benchmarks
        gradient_time = @belapsed gradient_finite_difference($u, $dx) samples=suite.repetitions evals=1
        
        # Interpolation benchmarks
        interp_time = @belapsed interpolate_bilinear($u, $(n÷2 + 0.5), $(n÷2 + 0.5)) samples=suite.repetitions evals=1
        
        # Memory allocation tracking
        laplacian_allocs = @allocated laplacian_finite_difference(u, dx)
        fft_allocs = @allocated laplacian_fft(u)
        
        # Store results
        size_key = "$(n)x$(n)"
        results[size_key] = Dict(
            "laplacian_fd_time" => laplacian_fd_time,
            "laplacian_fft_time" => laplacian_fft_time,
            "gradient_time" => gradient_time,
            "interpolation_time" => interp_time,
            "laplacian_fd_allocs" => laplacian_allocs,
            "laplacian_fft_allocs" => fft_allocs,
            "grid_points" => n^2,
            "memory_per_point" => sizeof(Float64) * n^2
        )
        
        if verbose
            println("Laplacian FD: $(round(laplacian_fd_time*1000, digits=2))ms, " *
                   "FFT: $(round(laplacian_fft_time*1000, digits=2))ms")
        end
    end
    
    suite.results["grid_operations"] = results
end

"""
    laplacian_finite_difference(u, dx)

Finite difference Laplacian for benchmarking.
"""
function laplacian_finite_difference(u, dx)
    nx, ny = size(u)
    laplacian = zeros(nx, ny)
    
    for j in 2:ny-1, i in 2:nx-1
        laplacian[i,j] = (u[i-1,j] + u[i+1,j] + u[i,j-1] + u[i,j+1] - 4*u[i,j]) / dx^2
    end
    
    return laplacian
end

"""
    laplacian_fft(u)

FFT-based Laplacian for benchmarking.
"""
function laplacian_fft(u)
    nx, ny = size(u)
    kx = 2π * fftfreq(nx, nx)
    ky = 2π * fftfreq(ny, ny)
    
    Kx = kx' .* ones(ny)
    Ky = ones(nx)' .* ky
    K2 = Kx.^2 + Ky.^2
    
    u_fft = fft(u)
    laplacian_fft = -K2 .* u_fft
    
    return real.(ifft(laplacian_fft))
end

"""
    gradient_finite_difference(u, dx)

Finite difference gradient for benchmarking.
"""
function gradient_finite_difference(u, dx)
    nx, ny = size(u)
    grad_x = zeros(nx, ny)
    grad_y = zeros(nx, ny)
    
    for j in 2:ny-1, i in 2:nx-1
        grad_x[i,j] = (u[i+1,j] - u[i-1,j]) / (2*dx)
        grad_y[i,j] = (u[i,j+1] - u[i,j-1]) / (2*dx)
    end
    
    return grad_x, grad_y
end

"""
    interpolate_bilinear(u, x, y)

Simple bilinear interpolation for benchmarking.
"""
function interpolate_bilinear(u, x, y)
    nx, ny = size(u)
    
    i = clamp(Int(floor(x)), 1, nx-1)
    j = clamp(Int(floor(y)), 1, ny-1)
    
    fx = x - i
    fy = y - j
    
    return (1-fx)*(1-fy)*u[i,j] + fx*(1-fy)*u[i+1,j] + 
           (1-fx)*fy*u[i,j+1] + fx*fy*u[i+1,j+1]
end

"""
    benchmark_time_integration!(suite::BenchmarkSuite, verbose::Bool)

Benchmark time integration methods.
"""
function benchmark_time_integration!(suite::BenchmarkSuite, verbose::Bool)
    results = Dict{String, Any}()
    
    for n in suite.problem_sizes
        if verbose
            print("  Time integration $n×$n: ")
        end
        
        # Setup test problem (simple diffusion)
        u = randn(n, n)
        u_new = similar(u)
        dt = 0.001
        D = 0.1
        dx = 1.0 / n
        
        # Forward Euler benchmark
        euler_time = @belapsed forward_euler_step!($u, $u_new, $dt, $D, $dx) samples=suite.repetitions evals=1
        
        # RK4 benchmark (simplified)
        rk4_time = @belapsed rk4_step!($u, $u_new, $dt, $D, $dx) samples=suite.repetitions evals=1
        
        # Semi-implicit benchmark
        semi_implicit_time = @belapsed semi_implicit_step!($u, $u_new, $dt, $D, $dx) samples=suite.repetitions evals=1
        
        results["$(n)x$(n)"] = Dict(
            "forward_euler_time" => euler_time,
            "rk4_time" => rk4_time,
            "semi_implicit_time" => semi_implicit_time,
            "grid_points" => n^2
        )
        
        if verbose
            println("Euler: $(round(euler_time*1000, digits=2))ms, " *
                   "RK4: $(round(rk4_time*1000, digits=2))ms, " *
                   "Semi-implicit: $(round(semi_implicit_time*1000, digits=2))ms")
        end
    end
    
    suite.results["time_integration"] = results
end

"""
    forward_euler_step!(u, u_new, dt, D, dx)

Forward Euler time step for benchmarking.
"""
function forward_euler_step!(u, u_new, dt, D, dx)
    nx, ny = size(u)
    
    for j in 2:ny-1, i in 2:nx-1
        laplacian = (u[i-1,j] + u[i+1,j] + u[i,j-1] + u[i,j+1] - 4*u[i,j]) / dx^2
        u_new[i,j] = u[i,j] + dt * D * laplacian
    end
    
    # Handle boundaries (simplified)
    u_new[1, :] = u_new[2, :]
    u_new[end, :] = u_new[end-1, :]
    u_new[:, 1] = u_new[:, 2]
    u_new[:, end] = u_new[:, end-1]
end

"""
    rk4_step!(u, u_new, dt, D, dx)

Simplified RK4 step for benchmarking.
"""
function rk4_step!(u, u_new, dt, D, dx)
    # Simplified RK4 implementation
    k1 = similar(u)
    k2 = similar(u)
    k3 = similar(u)
    k4 = similar(u)
    
    # Just do multiple Euler-like steps for timing comparison
    forward_euler_step!(u, k1, dt/2, D, dx)
    forward_euler_step!(k1, k2, dt/2, D, dx)
    forward_euler_step!(k2, k3, dt, D, dx)
    forward_euler_step!(k3, k4, dt, D, dx)
    
    u_new .= (k1 + 2*k2 + 2*k3 + k4) / 6
end

"""
    semi_implicit_step!(u, u_new, dt, D, dx)

Semi-implicit step for benchmarking.
"""
function semi_implicit_step!(u, u_new, dt, D, dx)
    # Simplified semi-implicit (just explicit for timing)
    forward_euler_step!(u, u_new, dt, D, dx)
end

"""
    benchmark_memory_usage!(suite::BenchmarkSuite, verbose::Bool)

Analyze memory usage patterns.
"""
function benchmark_memory_usage!(suite::BenchmarkSuite, verbose::Bool)
    results = Dict{String, Any}()
    
    if verbose
        println("  Memory allocation analysis:")
    end
    
    for n in suite.problem_sizes
        # Measure allocations for typical operations
        allocation_tests = Dict{String, Int}()
        
        # Array creation
        allocation_tests["array_creation"] = @allocated zeros($n, $n)
        
        # Array operations
        u = randn(n, n)
        v = randn(n, n)
        allocation_tests["array_addition"] = @allocated ($u .+ $v)
        allocation_tests["array_multiplication"] = @allocated ($u .* $v)
        
        # FFT operations
        allocation_tests["fft_forward"] = @allocated fft($u)
        u_fft = fft(u)
        allocation_tests["fft_inverse"] = @allocated ifft($u_fft)
        
        # Memory usage analysis
        element_size = sizeof(Float64)
        array_memory = element_size * n^2
        
        results["$(n)x$(n)"] = Dict(
            "allocations" => allocation_tests,
            "theoretical_memory" => array_memory,
            "grid_points" => n^2,
            "memory_efficiency" => allocation_tests["array_creation"] / array_memory
        )
        
        if verbose
            println("    $(n)×$(n): $(round(array_memory/1024/1024, digits=1)) MB theoretical, " *
                   "$(round(allocation_tests["array_creation"]/1024/1024, digits=1)) MB actual")
        end
    end
    
    suite.results["memory_analysis"] = results
end

"""
    benchmark_pattern_formation!(suite::BenchmarkSuite, verbose::Bool)

Benchmark pattern formation algorithms.
"""
function benchmark_pattern_formation!(suite::BenchmarkSuite, verbose::Bool)
    results = Dict{String, Any}()
    
    if verbose
        println("  Pattern formation performance:")
    end
    
    # This is a simplified benchmark - would integrate with actual pattern formation code
    for n in suite.problem_sizes
        # Mock Turing system evolution
        u = 0.5 .+ 0.1 * randn(n, n)
        v = 0.5 .+ 0.1 * randn(n, n)
        
        evolution_time = @belapsed turing_evolution_step!($u, $v, 0.001, $n) samples=suite.repetitions evals=1
        
        results["$(n)x$(n)"] = Dict(
            "evolution_time" => evolution_time,
            "grid_points" => n^2,
            "time_per_point" => evolution_time / n^2
        )
        
        if verbose
            println("    $(n)×$(n): $(round(evolution_time*1000, digits=2))ms " *
                   "($(round(evolution_time/n^2*1e9, digits=2)) ns/point)")
        end
    end
    
    suite.results["pattern_formation"] = results
end

"""
    turing_evolution_step!(u, v, dt, n)

Mock Turing evolution step for benchmarking.
"""
function turing_evolution_step!(u, v, dt, n)
    dx = 1.0 / n
    Du, Dv = 0.1, 1.0
    a, b = 0.1, 0.9
    
    u_new = similar(u)
    v_new = similar(v)
    
    for j in 2:n-1, i in 2:n-1
        # Laplacians
        lap_u = (u[i-1,j] + u[i+1,j] + u[i,j-1] + u[i,j+1] - 4*u[i,j]) / dx^2
        lap_v = (v[i-1,j] + v[i+1,j] + v[i,j-1] + v[i,j+1] - 4*v[i,j]) / dx^2
        
        # Reactions (Schnakenberg)
        reaction_u = a - u[i,j] + u[i,j]^2 * v[i,j]
        reaction_v = b - u[i,j]^2 * v[i,j]
        
        # Evolution
        u_new[i,j] = u[i,j] + dt * (Du * lap_u + reaction_u)
        v_new[i,j] = v[i,j] + dt * (Dv * lap_v + reaction_v)
    end
    
    # Periodic boundaries (simplified)
    u_new[1,:] = u_new[end-1,:]
    u_new[end,:] = u_new[2,:]
    u_new[:,1] = u_new[:,end-1]
    u_new[:,end] = u_new[:,2]
    
    v_new[1,:] = v_new[end-1,:]
    v_new[end,:] = v_new[2,:]
    v_new[:,1] = v_new[:,end-1]
    v_new[:,end] = v_new[:,2]
    
    u .= u_new
    v .= v_new
end

# Add placeholder functions for other benchmark categories
function benchmark_phase_field!(suite::BenchmarkSuite, verbose::Bool)
    # Placeholder for phase field benchmarks
    if verbose
        println("  Phase field benchmarks: [Placeholder]")
    end
    suite.results["phase_field"] = Dict("placeholder" => true)
end

function benchmark_fluid_dynamics!(suite::BenchmarkSuite, verbose::Bool)
    # Placeholder for fluid dynamics benchmarks
    if verbose
        println("  Fluid dynamics benchmarks: [Placeholder]")
    end
    suite.results["fluid_dynamics"] = Dict("placeholder" => true)
end

"""
    analyze_performance(suite::BenchmarkSuite; create_plots=true)

Analyze benchmark results and generate performance insights.
"""
function analyze_performance(suite::BenchmarkSuite; create_plots=true)
    println("📈 Performance Analysis Results")
    println("=" * 35)
    
    analysis = Dict{String, Any}()
    
    # Analyze grid operations
    if haskey(suite.results, "grid_operations") && !isempty(suite.results["grid_operations"])
        grid_analysis = analyze_grid_performance(suite.results["grid_operations"])
        analysis["grid_operations"] = grid_analysis
        
        println("Grid Operations Analysis:")
        println("  Laplacian FD scaling exponent: $(round(grid_analysis["laplacian_fd_scaling"], digits=3))")
        println("  Laplacian FFT scaling exponent: $(round(grid_analysis["laplacian_fft_scaling"], digits=3))")
        println("  FFT advantage factor: $(round(grid_analysis["fft_advantage"], digits=2))×")
    end
    
    # Analyze time integration
    if haskey(suite.results, "time_integration") && !isempty(suite.results["time_integration"])
        time_analysis = analyze_time_integration_performance(suite.results["time_integration"])
        analysis["time_integration"] = time_analysis
        
        println("Time Integration Analysis:")
        println("  Most efficient method: $(time_analysis["fastest_method"])")
        println("  Performance ratio (fastest/slowest): $(round(time_analysis["performance_ratio"], digits=2))")
    end
    
    # Memory analysis
    if haskey(suite.results, "memory_analysis") && !isempty(suite.results["memory_analysis"])
        memory_analysis = analyze_memory_performance(suite.results["memory_analysis"])
        analysis["memory_analysis"] = memory_analysis
        
        println("Memory Analysis:")
        println("  Average allocation efficiency: $(round(memory_analysis["avg_efficiency"]*100, digits=1))%")
        println("  Memory scaling exponent: $(round(memory_analysis["scaling_exponent"], digits=3))")
    end
    
    # Generate performance plots
    if create_plots
        performance_plots = create_performance_plots(suite.results)
        analysis["plots"] = performance_plots
        
        display(performance_plots)
    end
    
    # Generate recommendations
    recommendations = generate_performance_recommendations(analysis)
    analysis["recommendations"] = recommendations
    
    println("\nPerformance Recommendations:")
    for (i, rec) in enumerate(recommendations)
        println("  $i. $rec")
    end
    
    return analysis
end

"""
    analyze_grid_performance(grid_results)

Analyze grid operation performance scaling.
"""
function analyze_grid_performance(grid_results)
    sizes = [parse(Int, split(k, "x")[1]) for k in keys(grid_results)]
    sort_idx = sortperm(sizes)
    sizes = sizes[sort_idx]
    
    # Extract timing data
    fd_times = [grid_results["$(n)x$(n)"]["laplacian_fd_time"] for n in sizes]
    fft_times = [grid_results["$(n)x$(n)"]["laplacian_fft_time"] for n in sizes]
    
    # Fit scaling laws (assuming T ∝ N^α)
    log_sizes = log.(sizes.^2)  # Total grid points
    
    fd_scaling = fit_scaling_exponent(log_sizes, log.(fd_times))
    fft_scaling = fit_scaling_exponent(log_sizes, log.(fft_times))
    
    # Calculate advantage
    fft_advantage = mean(fd_times ./ fft_times)
    
    return Dict(
        "laplacian_fd_scaling" => fd_scaling,
        "laplacian_fft_scaling" => fft_scaling,
        "fft_advantage" => fft_advantage,
        "sizes" => sizes,
        "fd_times" => fd_times,
        "fft_times" => fft_times
    )
end

"""
    fit_scaling_exponent(log_x, log_y)

Fit scaling exponent using linear regression on log-log data.
"""
function fit_scaling_exponent(log_x, log_y)
    n = length(log_x)
    slope = (n * sum(log_x .* log_y) - sum(log_x) * sum(log_y)) / 
            (n * sum(log_x.^2) - sum(log_x)^2)
    return slope
end

"""
    analyze_time_integration_performance(time_results)

Analyze time integration method performance.
"""
function analyze_time_integration_performance(time_results)
    methods = ["forward_euler_time", "rk4_time", "semi_implicit_time"]
    method_names = ["Forward Euler", "RK4", "Semi-implicit"]
    
    # Average performance across problem sizes
    avg_times = Dict{String, Float64}()
    for method in methods
        times = [time_results[k][method] for k in keys(time_results)]
        avg_times[method] = mean(times)
    end
    
    # Find fastest method
    fastest_method_key = argmin(avg_times)
    fastest_method = method_names[findfirst(==(fastest_method_key), methods)]
    
    # Performance ratio
    slowest_time = maximum(values(avg_times))
    fastest_time = minimum(values(avg_times))
    performance_ratio = slowest_time / fastest_time
    
    return Dict(
        "fastest_method" => fastest_method,
        "performance_ratio" => performance_ratio,
        "average_times" => avg_times
    )
end

"""
    analyze_memory_performance(memory_results)

Analyze memory allocation performance.
"""
function analyze_memory_performance(memory_results)
    sizes = [parse(Int, split(k, "x")[1]) for k in keys(memory_results)]
    
    # Calculate efficiency (actual/theoretical)
    efficiencies = [memory_results["$(n)x$(n)"]["memory_efficiency"] for n in sizes]
    avg_efficiency = mean(efficiencies)
    
    # Memory scaling
    theoretical_memories = [memory_results["$(n)x$(n)"]["theoretical_memory"] for n in sizes]
    scaling_exponent = fit_scaling_exponent(log.(sizes.^2), log.(theoretical_memories))
    
    return Dict(
        "avg_efficiency" => avg_efficiency,
        "scaling_exponent" => scaling_exponent,
        "efficiencies" => efficiencies
    )
end

"""
    create_performance_plots(results)

Create comprehensive performance visualization plots.
"""
function create_performance_plots(results)
    plots_list = []
    
    # Grid operations plot
    if haskey(results, "grid_operations") && !isempty(results["grid_operations"])
        grid_results = results["grid_operations"]
        sizes = [parse(Int, split(k, "x")[1]) for k in keys(grid_results)]
        sort_idx = sortperm(sizes)
        sizes = sizes[sort_idx]
        
        fd_times = [grid_results["$(n)x$(n)"]["laplacian_fd_time"] * 1000 for n in sizes]  # Convert to ms
        fft_times = [grid_results["$(n)x$(n)"]["laplacian_fft_time"] * 1000 for n in sizes]
        
        p1 = plot(sizes.^2, fd_times, label="Finite Difference", 
                 xlabel="Grid Points", ylabel="Time (ms)", 
                 title="Laplacian Performance", scale=:log10, marker=:circle)
        plot!(p1, sizes.^2, fft_times, label="FFT", marker=:square)
        
        push!(plots_list, p1)
    end
    
    # Time integration plot
    if haskey(results, "time_integration") && !isempty(results["time_integration"])
        time_results = results["time_integration"]
        sizes = [parse(Int, split(k, "x")[1]) for k in keys(time_results)]
        sort_idx = sortperm(sizes)
        sizes = sizes[sort_idx]
        
        euler_times = [time_results["$(n)x$(n)"]["forward_euler_time"] * 1000 for n in sizes]
        rk4_times = [time_results["$(n)x$(n)"]["rk4_time"] * 1000 for n in sizes]
        
        p2 = plot(sizes.^2, euler_times, label="Forward Euler",
                 xlabel="Grid Points", ylabel="Time (ms)",
                 title="Time Integration Performance", scale=:log10, marker=:circle)
        plot!(p2, sizes.^2, rk4_times, label="RK4", marker=:square)
        
        push!(plots_list, p2)
    end
    
    # Memory usage plot
    if haskey(results, "memory_analysis") && !isempty(results["memory_analysis"])
        memory_results = results["memory_analysis"]
        sizes = [parse(Int, split(k, "x")[1]) for k in keys(memory_results)]
        sort_idx = sortperm(sizes)
        sizes = sizes[sort_idx]
        
        theoretical = [memory_results["$(n)x$(n)"]["theoretical_memory"] / 1024^2 for n in sizes]  # MB
        actual = [memory_results["$(n)x$(n)"]["allocations"]["array_creation"] / 1024^2 for n in sizes]
        
        p3 = plot(sizes.^2, theoretical, label="Theoretical",
                 xlabel="Grid Points", ylabel="Memory (MB)",
                 title="Memory Usage", scale=:log10, marker=:circle)
        plot!(p3, sizes.^2, actual, label="Actual", marker=:square)
        
        push!(plots_list, p3)
    end
    
    # Combine plots
    if length(plots_list) > 0
        layout_dims = (length(plots_list), 1)
        combined_plot = plot(plots_list..., layout=layout_dims, size=(800, 200*length(plots_list)))
        return combined_plot
    end
    
    return nothing
end

"""
    generate_performance_recommendations(analysis)

Generate actionable performance recommendations.
"""
function generate_performance_recommendations(analysis)
    recommendations = String[]
    
    # Grid operations recommendations
    if haskey(analysis, "grid_operations")
        grid_analysis = analysis["grid_operations"]
        if grid_analysis["fft_advantage"] > 2.0
            push!(recommendations, "Use FFT-based methods for Laplacian operations ($(round(grid_analysis["fft_advantage"], digits=1))× faster)")
        end
        
        if grid_analysis["laplacian_fft_scaling"] < 1.5
            push!(recommendations, "FFT methods scale well - suitable for large problems")
        end
    end
    
    # Time integration recommendations
    if haskey(analysis, "time_integration")
        time_analysis = analysis["time_integration"]
        push!(recommendations, "Use $(time_analysis["fastest_method"]) for time integration (fastest tested method)")
    end
    
    # Memory recommendations
    if haskey(analysis, "memory_analysis")
        memory_analysis = analysis["memory_analysis"]
        if memory_analysis["avg_efficiency"] < 0.8
            push!(recommendations, "Consider memory-efficient algorithms - current efficiency: $(round(memory_analysis["avg_efficiency"]*100, digits=1))%")
        end
    end
    
    # General recommendations
    push!(recommendations, "Enable multi-threading for CPU-bound operations")
    push!(recommendations, "Consider GPU acceleration for large-scale simulations")
    push!(recommendations, "Profile memory allocations in inner loops")
    
    return recommendations
end

# Demo execution
if abspath(PROGRAM_FILE) == @__FILE__
    println("🚀 PDEJulia.jl Performance Benchmarking Demo")
    println("=" * 45)
    
    # Create and run benchmark suite
    demo_suite = BenchmarkSuite(
        "Demo Benchmarks",
        categories=[:grid_operations, :time_integration, :memory_usage],
        problem_sizes=[32, 64, 128],
        repetitions=3,
        warmup_runs=1
    )
    
    # Run benchmarks
    completed_suite = run_comprehensive_benchmarks(demo_suite, verbose=true)
    
    # Analyze results
    analysis_results = analyze_performance(completed_suite, create_plots=true)
    
    println("\n🎉 Benchmarking demo completed!")
    println("Use these tools for systematic performance analysis in your research.")
end